RETRACTED: Design and development of SER-UNet model for glaucoma image analysis

Abstract

Glaucoma is a life-threatening disease that must be diagnosed early before it causes blindness. It is a dangerous disease and challenging for ophthalmologists. Retinal diseases can be detected at earlier stages with the help of analyzing fundus images of the retina and through the retinal vessel segmentation process. However, traditional fully convolutional neural network-based equivalents have major drawbacks in segmentation such as a bifurcation breakup in the vascular map and lessening pixel connectivity of vessels. To overcome this drawback, we present the squeeze excitation residual UNet (SER-UNet) model for vessel segmentation. The proposed model uses a new type of residual block called SER residual blocks for vessel segmentation. Initially, the fundus image is read and downsampled by converting the input image into vector values. Then, it conducts segmentation by adding attention mechanism and residual structure into convolution blocks to find vessel regions accurately and aggregate the tiny vessel characteristics. It helps segment the image of the glaucoma affected region in the retina. Together with a pixelwise cross-entropy loss function, it shows excellent performance on fundus image segmentation. The performance of the proposed method is assessed with an accuracy of 98.90% and 98.31%, respectively, using the DRIVE and STARE datasets, respectively.